Course Snapshot
- Period: 2020–2025
- Audience: Undergraduate engineering students.
- Prerequisites: Basic calculus and algebra.
HKUST IEDA
IEDA2540: Statistics for Engineers
This course introduces statistical thinking for engineering systems: data description, probabilistic modeling, statistical estimation, uncertainty quantification, and evidence-based decisions.
Learning Outcomes
- Summarize and visualize engineering data effectively.
- Apply probability models to quantify uncertainty.
- Construct point estimates and confidence intervals.
- Run hypothesis tests and interpret p-values responsibly.
- Build and interpret basic linear regression models.
Lecture Modules
Materials
T0a
Course Introduction
Course goals, data-driven engineering decisions, and the role of statistics in the IEDA curriculum.
Open SlidesT0b
Sampling Foundations
Population vs. sample, sampling bias, and how sampling design impacts inference quality.
Open SlidesT1
Descriptive Statistics
Summary measures and visual diagnostics for understanding variation and central tendency.
Open SlidesT2
Probability Distributions
Discrete and continuous distributions, random variables, and model assumptions used in practice.
Open SlidesT3a
Random Samples
Sampling distributions, law of large numbers intuition, and central limit theorem viewpoints.
Open SlidesT3b
Point Estimation
Estimators, bias, variance, MSE tradeoffs, and principles for good estimator construction.
Open SlidesT4
Confidence Intervals
Interval construction, interpretation under repeated sampling, and common misuse patterns.
Open SlidesT5
Hypothesis Testing
Null/alternative setup, type I/II errors, p-values, power, and practical test selection.
Open SlidesT6
Comparing Multiple Samples
Inference with more than two groups, model assumptions, and interpretation of group differences.
Open SlidesT7
Linear Regression
Model fitting, coefficient interpretation, diagnostics, and prediction in engineering contexts.
Open Slides